class_docstr: Class for Hugging Face sequence classification models trained on a NLI dataset
  
  Attributes:
      model_path: path to HF model
      tokenizer_path: path to HF tokenizer
      name: string identifier for the model. Lowercase letters and numbers.
          No spaces/special characters except dashes.
      description: String description of the model.
      tasks: List of supported task strings
      details: Dictionary of additional details about the model
      model_class: Class used to initialise model
      tokenizer_class: Class used to initialise tokenizer
      device: Device for model. Defaults to "cuda" if available.
code: def calculate_probability(self, text, labels):
      batch_features = []
  
      hypothesis = [f"This example is {l}." for l in labels]
      features = self.tokenizer(
          [text] * len(hypothesis),
          hypothesis,
          return_tensors="pt",
          truncation=True,
          padding=True,
      ).to(self._model_device)
  
      logits = self.model(
          features["input_ids"], features["attention_mask"]
      )[0]
      entail_contradiction_logits = logits[:, [0, 2]]
      probs = entail_contradiction_logits.softmax(dim=1)
      prob_label_is_true = probs[:, 1]
      return prob_label_is_true.tolist()
  
file_path: backprop/backprop/models/hf_nli_model/model.py
imports: from typing import List, Dict
  from transformers import AutoModelForSequenceClassification, AutoTokenizer
  from backprop.models import HFModel
  import torch
label_desc: Use image node_id 4 for calling the HFNLIModel obj's underlying member method code with example usage: obj.calculate_probability(text, labels) and returns: prob_label_is_true